Highest Similarity Index Research Articles

Order-of-magnitude increased range of constant force adjustment via section optimization

An adjustable constant force environment is critical in engineering applications, including precision manipulation, surgical robots, and advanced manufacturing, all requiring a wider range of force regulation and adjustment. Traditional adjustable constant force mechanisms (ACFMs) have significant limitations in achieving a wide range of constant force (CF) adjustments due to factors like stress and interference. Existing ACFMs typically offer only a 2–4 times change in CF, which is insufficient. This research aims to provide an order-of-magnitude increase in CF adjustment range while remaining compact and preserving CF quality through section optimization. An analytical model demonstrates the efficacy of adjusting CF by an order-of-magnitude in prismatic and non-prismatic beams for CF adjustment method selection. Additionally, finite element analysis and design optimization of the non-prismatic serpentine beam with an out-of-plumbness imperfection angle and polynomial section description were conducted to maximize CF adjustability and quality. Experimental validation showed a 38 times change in CF adjustability for 5 percent variation in the CF, 18 times improvement in compactness, and high Energy Similarity Index SCF compared to the prismatic benchmark mechanism. This proposed system may be implemented in several applications, including load control, impact and vibration mitigation, space exercise, wearables, etc.

Adaptive deep residual network for image denoising across multiple noise levels in medical, nature, and satellite images

This research introduces the Adaptive Deep Residual Network (AdResNet), a deep convolutional neural network designed for effective image denoising in computer vision applications. Configured with the Adaptive White Shark Optimizer (AWSO), AdResNet removes noise while preserving key visual features. The model is tested on multiple noise types (Gaussian, Salt-and-Pepper, Poisson, and mixed noise) at various intensity levels, demonstrating versatility. Evaluations across medical, natural, and satellite images ensure its robustness for real-world applications. AdResNet achieves superior denoising results, with low Mean Squared Error (MSE), high Peak Signal-to-Noise Ratio (PSNR), and high Structural Similarity Index Measure (SSIM). For example, the model recorded average metrics of MSE 13.61, PSNR 48.81 dB, and SSIM 0.96 on medical images, highlighting its efficacy. These results confirm AdResNet’s suitability for applications requiring high image quality, such as medical and satellite imaging.

Biodiversity of Aspergillus Species and Their Mycotoxin Production Potential in Dry Meat.

This study aimed to examine fungi diversity in dried beef meat sold in Ekiti State, characterize the isolated fungi, and determine the aflatoxin-producing ability of the Aspergillus fungi in the samples. Dried beef meat was collected from different markets in Ekiti State and screened for the presence of filamentous fungi using molecular methods. Samples were cultured aseptically on potato dextrose agar (PDA) for fungi isolation, and molecular identification was performed using DNA extraction, Polymerase chain Reaction (PCR), ITS-1/ITS-4 primer pair, and nucleotide sequencing. The results obtained indicated a range of filamentous fungi genera including Aspergillus, Rhizopus, Penicillium, Fusarium, Cladosporium, Alternaria, and other fungi species contaminating the dried meat at (43%), (42%), (3%), (2%), (2%), (1%), and (7%), respectively. High incidences were recorded for Aspergillus flavus, Aspergillus niger, and Aspergillus fumigatus in most of the screened samples. Aspergillus flavus accounted for (24.7%) of all the Aspergillus species isolated with the presence of the gene needed for aflatoxin production. The occurrences of these filamentous fungal species pose a cause for concern, as most of these fungal species are known producers of certain toxic substances. Maximum likelihood phylogenetic analysis showed a high similarity index score, which indicated a good relationship between isolated Aspergillus Species and the closely related strains from GenBank, isolated from different sources and countries. The implication of this study is that consumer health may be at risk through exposure to contaminated dried meat.

Comparative Study of Lactogenic Effect and Milk Mutritional Density of Oral Galactagogues in Female Rabbit.

Hypogalactia and agalactia in lactating mothers are the major causes of child malnutrition, mortality, morbidity, and overall ill health. The development of such treatments requires a well-designed preclinical study with suitable laboratory animals, which needs to be improved. Thus, a suitably designed study with a laboratory animal to analyse galactagogue activity, along with an assessment of the quality and quantity of milk, is required. This study aimed to evaluate the potential of rabbits as an animal model for studying lactogenic activity. The structural homology of prolactin, prolactin, and prolactin in humans, rabbits, and rats was studied using BLAST and PyMol to assess similarity in the lactogenic system. Daily and cumulative milk production and pre-treatment (control) and post-treatment (three drugs) in rabbits were recorded and evaluated by analysing protein, fat, lactose, solid non-fat, and ash values. All parameters were recorded on the 0th day and at the end of weeks 1, 2, and 3. Mammary gland histopathology was performed to evaluate the effects on mammary glands. Homology studies revealed that the sequences of the human and rabbit prolactin genes, receptors, and hormones had a high similarity index. Treatment with Domperidone, Metoclopramide, and Shatavari significantly enhanced milk production by enhancing prolactin secretion; only Shatavari increased milk nutrition. Enlargement of the tubuloalveolar ducts of the mammary glands was observed. Our findings suggest that rabbits are robust, reproducible, ethically superior, and preclinically relevant animals for assessing lactogenic activity.

Employing Siamese Networks as Quantitative Biomarker for Assessing the Effect of Dolphin-Assisted Therapy on Pediatric Cerebral Palsy.

This study explores the potential of using a Siamese Network as a biomarker for assessing the effectiveness of Dolphin-Assisted Therapy (DAT) in children with Spastic Cerebral Palsy (SCP). The problem statement revolves around the need for objective measures to evaluate the impact of DAT on patients with SCP, considering the subjective nature of traditional assessment methods. The methodology involves training a Siamese network, a type of neural network designed to compare similarities between inputs, using data collected from SCP patients undergoing DAT sessions. The study employed Event-Related Potential (ERP) and Fast Fourier Transform (FFT) analyses to examine cerebral activity and brain rhythms, proposing the use of SNN to compare electroencephalographic (EEG) signals of children with cerebral palsy before and after Dolphin-Assisted Therapy. Testing on samples from four children yielded a high average similarity index of 0.9150, indicating consistent similarity metrics before and after therapy. The network is trained to learn patterns and similarities between pre- and post-therapy evaluations, in order to identify biomarkers indicative of therapy effectiveness. Notably, the Siamese Network's architecture ensures that comparisons are made within the same feature space, allowing for more accurate assessments. The results of the study demonstrate promising findings, indicating different patterns in the output of the Siamese Network that correlate with improvements in symptoms of SCP post-DAT. Confirming these observations will require large, longitudinal studies but such findings would suggest that the Siamese Network could have utility as a biomarker in monitoring treatment responses for children with SCP who undergo DAT and offer them more objective as well as quantifiable manners of assessing therapeutic interventions. Great discrepancies in neuronal voltage perturbations, 7.9825 dB on average at the specific samples compared to the whole dataset (6.2838 dB), imply a noted deviation from resting activity. These findings indicate that Dolphin-Assisted Therapy activates particular brain regions specifically during the intervention.

Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report

Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density data crucial for accurate dose calculations. However, accurately representing patient anatomy is challenging, especially in adaptive radiotherapy, where CT is not acquired daily. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast. Still, it lacks electron density information, while cone beam CT (CBCT) lacks direct electron density calibration and is mainly used for patient positioning.Adopting MRI-only or CBCT-based adaptive radiotherapy eliminates the need for CT planning but presents challenges. Synthetic CT (sCT) generation techniques aim to address these challenges by using image synthesis to bridge the gap between MRI, CBCT, and CT. The SynthRAD2023 challenge was organized to compare synthetic CT generation methods using multi-center ground truth data from 1080 patients, divided into two tasks: (1) MRI-to-CT and (2) CBCT-to-CT. The evaluation included image similarity and dose-based metrics from proton and photon plans.The challenge attracted significant participation, with 617 registrations and 22/17 valid submissions for tasks 1/2. Top-performing teams achieved high structural similarity indices (≥0.87/0.90) and gamma pass rates for photon (≥98.1%/99.0%) and proton (≥97.3%/97.0%) plans. However, no significant correlation was found between image similarity metrics and dose accuracy, emphasizing the need for dose evaluation when assessing the clinical applicability of sCT.SynthRAD2023 facilitated the investigation and benchmarking of sCT generation techniques, providing insights for developing MRI-only and CBCT-based adaptive radiotherapy. It showcased the growing capacity of deep learning to produce high-quality sCT, reducing reliance on conventional CT for treatment planning.

The Relationship of Terrestrial Fern Species (Pteridophyta) at Salodik Waterfall Central Sulawesi Based on Morphological Characteristics

Pteridophyta ferns are a group of vascular plants that have a very long evolutionary history. This study aims to describe the relationship of terrestrial fern plant species (Pteridophyta) at Salodik Waterfall Central Sulawesi based on morphological characteristics. The method used was exploration method with the sampling technique being free collection. The data were processed using Microsoft Excel 2013 and then subjected to cluster analysis using the UPGMA (Unweighted Pair-Group Method with Arithmetic Average) method in the PAST 4.03 software (Paleontological Statistics). The research results obtained a total of 13 terrestrial fern plant species, namely Asplenium nidus, Diplazium esculentum, Davallia denticulata, Lindsaea scandens var. terrestris, Nephrolepis biserrata, Angiopteris evecta, Phymatosorus longissima, Pteris biaurita, Pteris fauriei, Selaginella doederleinii, Chingia clavipilosa, Macrothelypteris torresiana and Sphaerostephanos heterocarpus. The dendrogram of kinship between 13 species of terrestrial ferns is formed in 2 main clusters with 4 relationships categories, namely very near, near, not near, and not very near. The first category of relationship is very near with the highest Similarity Index (IS) value, namely between Macrothelypteris torresiana with Davalia denticulata (IS=86.5) and Lindsaea scandens with Pteris faurei (IS=75.2). The second category of near relationship is between Pteris biaurita with node 3 (IS=68.2), Selaginella doederleinii with node 4 (IS=64), and Diplazium esculentum with Chingia clavipilosa (IS=55.6). The third category of not near relationship is between Nephrolepis biserrata with Phymatosorus longissima (IS=27.3). The fourth category of not very near relationship with the lowest Similarity Index (IS) value is between Angiopteris evecta with Sphaerostephanos heterocarpus (IS=22.8) and Asplenium nidus with sub division Ia (IS=21.2).

Integrating Image Perception and Time‐to‐First‐Spike Coding in MoS2 Phototransistors for Spiking Neural Network

AbstractHuman vision system remains alert for dangers and adopts high‐speed and low‐power coding methods to convert the image information to spike signals. To meet the demand for danger alert in machine vision, it is important to design intelligent sensors to integrate the functions of image perception and high‐efficiency coding for high‐priority analysis. Inspired by the human visual system, a MoS2 phototransistor is introduced on SiNx substrate enabling simultaneous image perception and time‐to‐first‐spike (TTFS) coding. The device demonstrates exceptional performance in encoding 3‐bit grayscale images, achieving a low mean squared error of 0.008 and a high structural similarity index of 0.9784. Spiking neural networks (SNN) with TTFS coding achieve high recognition accuracy (98.86%) while reducing spike count by 75%. The device array also perceives motion direction and object states by converting data temporally. This work establishes a hardware foundation to promote the performance of SNNs in efficiently identifying crucial information.

Identification of novel PPAR-γ agonist ameliorating triple negative breast cancer by in silico methods

Triple negative breast cancer (TNBC) is still considered a medical challenge as it is an aggressive cancer characterized by poor prognosis, frequent metastasis, distinct clinical and pathological features and lack of proper treatment options. In TNBC, estrogen, progesterone and Her2 neu receptors are absent, which are usually present in otherwise breast cancers. So, hormonal therapy cannot be given in case of TNBC, leaving chemotherapy as the mainstay for treatment. However, chemotherapy is an infamous therapeutic option due to several aweful side effects like loss of appetite, extreme weight loss, hair loss, vomiting, fatigue etc. Several reports have highlighted role of Peroxisome Proliferated Activated Receptor gamma (PPAR-γ) in ameliorating breast cancer, but it is still less explored receptor for TNBC. Moreover, synthetic PPAR gamma agonists like thiazolidinediones, have good capability of activating PPAR-γ receptor, but these are also gloomy therapeutic agent due to their dreadful side effects like hepatotoxicity, bladder cancer, congestive heart failure etc. So, there is serious urge among researchers to find safer therapeutic option for TNBC. Phytochemicals are nowadays grabbing attention worldwide to treat several diseases due to their fewer or no side effects. This in silico study was performed after thorough review to select a natural, potent PPAR-γ agonist Gallotanin, as a query compound for ligand based similarity searching in PubChem database with 80% filter. Top 10 obtained compounds having highest similarity index were chosen for molecular docking in AutoDock Vina, with PPAR-γ receptor (3V9T) to know their binding patterns. Further, top 5 compounds having highest dock scores underwent ADME studies in SwissADME to assess pharmacokinetic profile of these compounds. Finally, this in silico study resulted in discovery of compound called Chamuvaritin which is still not known as a PPAR-γ agonist, efficient enough to act as potent therapeutic agent against TNBC.

First Report of Natural Infection of Watermelon mosaic virus (WMV) Infecting Bottle Gourd and Snake Melon

Cucurbitaceous crops, one of the main crops of agriculture, are sensitive to many plant viruses. In August 2019, virus-like symptoms were observed on some cucurbit plants grown in private home gardens in Antalya and Denizli provinces (Turkey). A total of 53 leaf samples were sampled from plants with the most symptoms (melon (Cucumis melo L.), watermelon (Citrullus lanatus L.), bottle gourd (Lagenaria siceraria (Molina) Standl.), and snake melon (Cucumis melo var. flexuosus) and tested by Reverse-Transcriptase Polymerase Chain Reaction (RT-PCR) against possible watermelon mosaic potyvirus (WMV) infection. The coat protein gene (CP) specific primer sets amplified a gene product of nearly 820 bp fragment from symptomatic plants. WMV infections were detected in 31 individual cucurbit plants, including 11 melons, 8 watermelons, 7 snake melons and 5 bottle gourds. The presence of viral infection was found only in ornamental squash plants in Antalya province and in all cucurbits sampled in Denizli province. To better comprehend the molecular characteristics of virus isolates, the amplified viral DNA fragments were cloned in a proper prokaryotic plasmid, sequenced by Next Generation Sequencing (NGS) and recorded to GenBank. Bioinformatic analyses using the Basic Local Alignment Search Tool (BLAST) showed that the identified CP gene sequences exhibited significant nucleotide homogeneity, supported by a high nucleotide similarity index with that of other isolates around the world. In addition, Turkish isolates isolated from Antalya and Denizli regions showed approximately 94% nucleotide similarity among themselves. For phylogenetic inference, WMV sequences were subjected to multiple alignments with isolates from different geographic origins of the same viruses. Molecular phylogeny showed that all WMV isolates are closely related to other world WMV isolates at variable rates. WMV is wide host range viruses in cucurbit crops, however, this work is the first scientific report of WMV isolates detected in bottle gourd and snake melon from the South and West Regions of Turkey all over the world.WMV are broad spectrum viruses in cucurbit crops. This work is the first scientific report of Watermelon mosaic potyvirus isolates detected in bottle gourd and snake melon from the South-West Region of Turkey in all over the world.

Zoonotic linkage and environmental contamination of Methicillin-resistant Staphylococcus aureus (MRSA) in dairy farms: A one health perspective

Methicillin-Resistant Staphylococcus aureus (MRSA) is a ubiquitous public health challenge, with its prevalence in human, animal, and environmental interfaces posing significant concerns. This study aimed to characterize and detect the zoonotic linkages of MRSA within the cow-environment-human interfaces in dairy farms to address the One Health perspective. A comprehensive investigation, involving 636 samples (an equal number of raw milk and cow nasal swab samples, along with varying numbers of human nasal swab and environmental samples), revealed an overall MRSA prevalence of 13.4% (n = 271/636). Notably, environmental samples exhibited the highest prevalence (19.3%), emphasizing the potential role of farm surroundings in MRSA transmission, while the lowest prevalence was found in raw milk at 11.8% (n = 31/263). The prevalence in cow nasal swabs and human nasal swabs was 13.3% (n = 35/263) and 15.1% (n = 8/53), respectively. Multiplex PCR analysis revealed the presence of different Staphylococcal enterotoxin (SEa, SEb, SEc, and SEd), and exfoliative toxin-producing genes (Eta, Etb) within the MRSA isolates underlining their potential to induce public health threats. All MRSA isolates exhibited complete resistance to Oxacillin (100%) and Amoxicillin (100%), while the highest sensitivity was observed for Vancomycin (85.8%). Furthermore, these MRSA strains demonstrated varying degrees of resistance to other commonly used antimicrobial drugs, including Cefoxitin (75.3%), Ceftarolin (71.2%), Sulfamethoxazole-Trimethoprim (63.5%), Ciprofloxacin (60%), and Gentamicin (49.5%). Detection of MRSA in cow, human, and environmental samples within the same farm vicinity highlights the risk of zoonotic transmission of MRSA from cows to humans through environmental interfaces. Phylogenetic analysis of the mecA gene in MRSA isolates from all sources within the same farm revealed a high similarity index (>84%) among them suggesting a shared evolutionary origin. Moreover, the MRSA isolates from milk samples showed a close evolutionary relationship with isolates from Kenya and Brazil, while the isolates from humans and the environment displayed noticeable resemblance to isolates from several Asian countries. The findings emphasize the importance of collaborative efforts under the One Health framework to address this multifaceted issue and ensure the safety of our food supply and public health.

Deep learning model based on multi-scale feature fusion for precipitation nowcasting

Abstract. Forecasting heavy precipitation accurately is a challenging task for most deep learning (DL)-based models. To address this, we present a novel DL architecture called “multi-scale feature fusion” (MFF) that can forecast precipitation with a lead time of up to 3 h. The MFF model uses convolution kernels with varying sizes to create multi-scale receptive fields. This helps to capture the movement features of precipitation systems, such as their shape, movement direction, and speed. Additionally, the architecture utilizes the mechanism of discrete probability to reduce uncertainties and forecast errors, enabling it to predict heavy precipitation even at longer lead times. For model training, we use 4 years of radar echo data from 2018 to 2021 and 1 year of data from 2022 for model testing. We compare the MFF model with three existing extrapolative models: time series residual convolution (TSRC), optical flow (OF), and UNet. The results show that MFF achieves superior forecast skills with high probability of detection (POD), low false alarm rate (FAR), small mean absolute error (MAE), and high structural similarity index (SSIM). Notably, MFF can predict high-intensity precipitation fields at 3 h lead time, while the other three models cannot. Furthermore, MFF shows improvement in the smoothing effect of the forecast field, as observed from the results of radially averaged power spectral (RAPS). Our future work will focus on incorporating multi-source meteorological variables, making structural adjustments to the network, and combining them with numerical models to further improve the forecast skills of heavy precipitations at longer lead times.

Deep Learning-Based Prediction of Radiation Therapy Dose Distributions in Nasopharyngeal Carcinomas: A Preliminary Study Incorporating Multiple Features Including Images, Structures, and Dosimetry.

Purpose: Intensity-modulated radiotherapy (IMRT) is currently the most important treatment method for nasopharyngeal carcinoma (NPC). This study aimed to enhance prediction accuracy by incorporating dose information into a deep convolutional neural network (CNN) using a multichannel input method. Methods: A target conformal plan (TCP) was created based on the maximum planning target volume (PTV). Input data included TCP dose distribution, images, target structures, and organ-at-risk (OAR) information. The role of target conformal plan dose (TCPD) was assessed by comparing the TCPD-CNN (with dose information) and NonTCPD-CNN models (without dose information) using statistical analyses with the ranked Wilcoxon test (P < .05 considered significant). Results: The TCPD-CNN model showed no statistical differences in predicted target indices, except for PTV60, where differences in the D98% indicator were < 0.5%. For OARs, there were no significant differences in predicted results, except for some small-volume or closely located OARs. On comparing TCPD-CNN and NonTCPD-CNN models, TCPD-CNN's dose-volume histograms closely resembled clinical plans with higher similarity index. Mean dose differences for target structures (predicted TCPD-CNN and NonTCPD-CNN results) were within 3% of the maximum prescription dose for both models. TCPD-CNN and NonTCPD-CNN outcomes were 67.9% and 54.2%, respectively. 3D gamma pass rates of the target structures and the entire body were higher in TCPD-CNN than in the NonTCPD-CNN models (P < .05). Additional evaluation on previously unseen volumetric modulated arc therapy plans revealed that average 3D gamma pass rates of the target structures were larger than 90%. Conclusions: This study presents a novel framework for dose distribution prediction using deep learning and multichannel input, specifically incorporating TCPD information, enhancing prediction accuracy for IMRT in NPC treatment.

Editorial

Editorial

Two‐stage diffusion model deriving FDG‐PET from T1 Weighted Magnetic Resonance Images for diagnosis of Alzheimer’s disease

AbstractBackgroundAlzheimer’s Disease (AD) is a neurodegenerative disease which is characterized by cognitive and functional impairment, and defined by triad of Amyloid status, Tau pathology, and Neuroimaging marker of neurodegeneration or neuronal injury. An early diagnosis of AD can be challenging and it can be detected earlier in brain functional imaging than brain structural imaging.MethodWe develop a pipeline for synthesizing FDG‐PET/MRI from source MRI‐T1‐Weighted (MRI‐T1W) images based on denoising diffusion mechanism [1]. The pipeline includes a forward pass which encodes the source MRI‐T1WI images to gaussian noise space, and a backward pass which generates the corresponding FDG‐PET from the derived noises with repeated denoising steps. Furthermore, to validate the effectiveness of the synthesized FDG‐PET images, we also develop multiple types of classifiers and the experiments for both single input of MRI‐T1WI and joint input of MRI‐T1WI and synthesized FDG‐PET.ResultThe experiments are performed on 1036 of FDG‐PET/MRI pairs (286,474, 276 for Cognitive Normal, Mild Cognitive Impaiement and Alzheimer’s Disease each) are obtained from ADNI [2] datasets and display the promising results with low Mean Absolute Error (MAE) and high Structural Similarity Index Measure (SSIM) for the two stage diffusion denoising based FDG‐PET synthesis pipeline. The results for the additional classification experiments also show that in most cases the joint input enhances the classifiers' performance in accuracy and F1‐score. The results also indicate that the synthesized FDG‐PET images are not only visually similar to the ground truth images, but also improve the performance of machine learning based AD diagnosis.ConclusionThis work provides support for the notion that machine learning‐derived image analysis may be a useful approach to improving the diagnosis of AD.Reference:[1] Ho, J., Jain, A., &amp; Abbeel, P. (2020). Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33, 6840‐6851.[2] Petersen, R., Aisen, P. S., Beckett, L. A., Donohue, M. C., Gamst, A. C., Harvey, D. J., … &amp; Toga, A. W. (2010). Alzheimer’s disease neuroimaging initiative (ADNI). Neurology, 74(3), 201‐209.

Text duplication of papers in four medical related fields

Abstract Purpose To reveal the typical features of text duplication in papers from four medical fields: basic medicine, health management, pharmacology and pharmacy, and public health and preventive medicine. To analyze the reasons for duplication and provide suggestions for the management of medical academic misconduct. Design/methodology/approach In total, 2,469 representative Chinese journal papers were included in our research, which were submitted by researchers in 2020 and 2021. A plagiarism check was carried out using the Academic Misconduct Literature Check System (AMLC). We generated a corrected similarity index based on the AMLC general similarity index for further analysis. We compared the similarity indices of papers in four medical fields and revealed their trends over time; differences in similarity index between review and research articles were also analyzed according to the different fields. Further analysis of 143 papers suspected of plagiarism was also performed from the perspective of sections containing duplication and according to the field of research. Findings Papers in the field of pharmacology and pharmacy had the highest similarity index (8.67 ± 5.92%), which was significantly higher than that in other fields, except health management. The similarity index of review articles (9.77 ± 10.28%) was significantly higher than that of research articles (7.41 ± 6.26%). In total, 143 papers were suspected of plagiarism (5.80%) with similarity indices ≥ 15%; most were papers on health management (78, 54.55%), followed by public health and preventive medicine (38, 26.58%); 90.21% of the 143 papers had duplication in multiple sections, while only 9.79% had duplication in a single section. The distribution of sections with duplication varied among different fields; papers in pharmacology and pharmacy were more likely to have duplication in the data/methods and introduction/background sections, however, papers in health management were more likely to contain duplication in the introduction/background or results/discussion sections. Different structures for papers in different fields may have caused these differences. Research limitations There were three limitations to our research. Firstly, we observed that a small number of papers have been checked early. It is unknown who conducted the plagiarism check as this can be included in other evaluations, such as applications for Science and technology projects or awards. If the authors carried out the check, text with high similarity indices may have been excluded before submission, meaning the similarity index in our research may have been lower than the original value. Secondly, there were only four medical fields included in our research. Additional analysis on a wider scale is required in the future. Thirdly, only a general similarity index was calculated in our study; other similarity indices were not tested. Practical implications A comprehensive analysis of similarity indices in four medical fields was performed. We made several recommendations for the supervision of medical academic misconduct and the formation of criteria for defining suspected plagiarism for medical papers, as well as for the improved accuracy of text duplication checks. Originality/value We quantified the differences between the AMLC general similarity index and the corrected index, described the situation around text duplication and plagiarism in papers from four medical fields, and revealed differences in similarity indices between different article types. We also revealed differences in the sections containing duplication for papers with suspected plagiarism among different fields.

Isolation, screening, and characterization of plant growth enhancing endophytic bacteria from halophytic Heliotropium curassavicum L. collected from salt stress areas of Srikakulam, Andhra Pradesh

Farmers use excessive chemical fertilizers to boost crop productivity to meet growing agricultural demands. However, this practice is costly and environmentally hazardous. Sustainable increase in crop yield can be achieved through alternatives like microbial-based fertilizers. In the quest to identify plant growth-promoting endophytic bacteria, the present study was carried out and selected unexplored halophytic plant Heliotropium curassavicum L. Thirteen endophytic bacterial strains were isolated from both aerial and root portions of H.curassavicum. These isolates were tested for salt tolerance, enzyme production, and synthesis of growth-promoting secondary metabolites, like Indole-3-acetic acid (IAA) and phosphate solubilization . Most of the isolates belonged to the Bacillus family, exhibiting varying Gram staining and biochemical reactions. The majority are Gram-positive bacteria, non-motile, spore formers, and exist in two cells or chains. All isolates could tolerate up to 10% NaCl concentration and a temperature of 42°C. Based on phenotypic, bio-chemical characteristics, isolate HCR3 showed promising properties in synthesizing IAA and phosphate solubilization abilities. The isolate HCR3 grew well upto 10% NaCl concentration and also 42°C temperature. Based on molecular characterization by using 16S rRNA gene-based analysis HCR3 isolate was identified and belonged to the Genus Pseudomonas with the highest similarity index with Pseudomonas khazarica sp. HCR3 showed IAA production of 37µg ml-1, had a phosphate solubilization ability of 3.5 ppm, and recorded protease activity on gelatin medium. The findings highlight the potential of HCR3 and other strains from halophytic H. curassavicum L. to enhance plant growth through secondary bioactive metabolites, offering eco-friendly solutions for sustainable agriculture.

An efficient deep multi‐task learning structure for covid‐19 disease

AbstractCOVID‐19 has had a profound global impact, necessitating the development of infection detection systems based on machine learning. This paper presents a Multi‐task architecture that addresses the classification and segmentation tasks for COVID‐19 detection. The model comprises an encoder for feature representation, a decoder for segmentation, and a multi‐layer perceptron for classification. Evaluations conducted on two datasets demonstrate the model's performance in both classification and segmentation. To enhance efficiency and diagnosis accuracy, CT‐scan images undergo pre‐processing using image processing algorithms like histogram equalization, median filtering, and mathematical morphology operations. The combination of the median filter pre‐processing and the proposed model yields impressive results in the classification task, achieving high accuracy, sensitivity, and specificity, with values of 0.97, 0.97, and 0.96, respectively, for dataset 1, and 0.96 in mentioned metrics for dataset 2. For segmentation, the proposed model, particularly with the average morphology pre‐processing, exhibits excellent performance with high accuracy, low mean squared error, high peak signal‐to‐noise ratio, high structural similarity index, and a mean dice coefficient of 88.86 ± 0.05 for dataset 1, and 87.97 ± 0.02 for dataset 2. Furthermore, the pre‐trained models consistently demonstrate the superiority of the median filter and proposed model in the classification task on the same datasets. In conclusion, the proposed multi‐task model, incorporating image processing techniques, achieves remarkable results in both classification and segmentation. The utilization of pre‐processing algorithms and the multi‐task framework significantly contribute to superior performance metrics. This study encourages further exploration of combining diverse image processing algorithms to advance infection diagnosis and treatment.

Molecular Characterization and Antimicrobial Susceptibilities of Corynebacterium pseudotuberculosis Isolated from Caseous Lymphadenitis of Smallholder Sheep and Goats.

Caseous lymphadenitis (CLA) is a bacterial infection caused by Corynebacterium pseudotuberculosis (C. pseudotuberculosis) that affects sheep and goats, leading to abscess formation in their lymph nodes. The present study aimed to isolate and identify C. pseudotuberculosis from CLA in smallholder sheep and goats, and determine the resistance patterns, virulence, and resistance genes of the isolates. Additionally, genotypic and phylogenetic analysis of the isolates was conducted using ERIC-PCR and DNA sequencing techniques. A cross-sectional study examined 220 animals (130 sheep and 90 goats) from 39 smallholder flocks for clinical signs of CLA. Fifty-four (24.54%) animals showed CLA-compatible lesions, confirmed by C. pseudotuberculosis isolation and PCR identification. Sheep had a lower infection rate of CLA (18.46%) compared with goats (33.3%). Antimicrobial susceptibility testing of 54 C. pseudotuberculosis isolates to 24 antimicrobial drugs revealed that they were 100% resistant to bacitracin and florfenicol, while none of the isolates were resistant to norfloxacin. A high resistance rate was observed for penicillin and erythromycin (92.6% each). Interestingly, 16.7% of C. pseudotuberculosis isolates recovered from sheep showed vancomycin resistance. Molecular characterization of C. pseudotuberculosis isolates revealed that PLD, PIP, and FagA virulence genes were present in all examined isolates. However, the FagB, FagC, and FagD genes were detected in 24 (100%), 20 (83%), and 18 (75%) of the sheep isolates, and 26 (87%), 26 (87%), and 18 (60%) of the goat isolates, respectively. The β-lactam resistance gene was present in all isolates. Furthermore, 83% of the sheep isolates carried the aminoglycoside (aph(3″)-lb), chloramphenicol (cat1), and bacitracin (bcrA) resistance genes. Among the isolates recovered from goats, 73% were found to contain macrolides (ermX), sulfonamide (sul1), and bacitracin (bcrA) resistance genes. It is worrisome that the glycopeptide (vanA) resistance gene was detected in 8% of the sheep isolates as a first report. ERIC-PCR genotyping of 10 multi-drug-resistant C. pseudotuberculosis isolates showed a high similarity index of 83.6% between isolates from sheep and goats. Nucleotide sequence analysis of partial 16S rRNA sequences of C. pseudotuberculosis revealed 98.83% similarity with biovar Ovis of globally available reference sequences on the Genbank database. Overall, our findings might indicate that C. pseudotuberculosis infection in smallholders in Egypt might be underestimated despite the significant financial impact on animal husbandry and potential health hazards it poses. Moreover, this study highlights the importance of implementing a sustainable control strategy and increasing knowledge and awareness among smallholder breeders to mitigate the economic impact of CLA.

Single-shot quantitative phase contrast imaging based on deep learning.

Quantitative differential phase-contrast (DPC) imaging is one of the commonly used methods for phase retrieval. However, quantitative DPC imaging requires several pairwise intensity measurements, which makes it difficult to monitor living cells in real-time. In this study, we present a single-shot quantitative DPC imaging method based on the combination of deep learning (DL) and color-encoded illumination. Our goal is to train a model that can generate an isotropic quantitative phase image (i.e., target) directly from a single-shot intensity measurement (i.e., input). The target phase image was reconstructed using a linear-gradient pupil with two-axis measurements, and the model input was the measured color intensities obtained from a radially asymmetric color-encoded illumination pattern. The DL-based model was trained, validated, and tested using thirteen different cell lines. The total number of training, validation, and testing images was 264 (10 cells), 10 (1 cell), and 40 (2 cells), respectively. Our results show that the DL-based phase images are visually similar to the ground-truth phase images and have a high structural similarity index (>0.98). Moreover, the phase difference between the ground-truth and DL-based phase images was smaller than 13%. Our study shows the feasibility of using DL to generate quantitative phase imaging from a single-shot intensity measurement.